In [1]:
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.colors import to_hex
import seaborn as sns
import numpy as np
import os
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.manifold import TSNE
from scipy.cluster.hierarchy import linkage, dendrogram
from scipy.stats import ttest_ind
from statsmodels.stats.multitest import multipletests
In [2]:
df = pd.read_csv('C:/Users/Lympha/Desktop/temp_dir/result_dataframes/pyrosetta_vanderwaals2_dataframe.csv')
In [3]:
print(df.info)

<bound method DataFrame.info of     Unnamed: 0    pos1:M    pos2:T    pos3:E     pos4:Y     pos5:K     pos6:L  \
0         1A2B  2.779494  1.346638  1.505610        NaN   4.792497   2.745932   
1         1AA9  0.682297  8.415422  5.525405  11.984290  13.249885  20.284461   
2         1AGP  6.256867  0.542625  1.078158   1.291350   0.461579   1.732784   
3         1AM4       NaN       NaN       NaN  12.195632        NaN        NaN   
4         1AN0  1.145109       NaN       NaN        NaN        NaN        NaN   
..         ...       ...       ...       ...        ...        ...        ...   
376       8DNJ  0.981081  0.633802  0.643428   1.328113   2.103719   1.133679   
377       8EBZ       NaN       NaN       NaN        NaN        NaN   0.031832   
378       8EZG  6.499718  0.314923  0.853837   3.933503   7.116811   6.087974   
379       8F0M       NaN       NaN  0.020218        NaN        NaN        NaN   
380       8IJ9  0.012935  1.246463  1.178675   4.646635   0.613046   3.000449   

        pos7:V     pos8:V     pos9:V  ...  pos180:G  pos181:C  pos182:M  \
0     1.606917   3.031467   1.573611  ...       NaN       NaN       NaN   
1    35.385963  25.245161   9.270932  ...       NaN       NaN       NaN   
2     2.535707   0.765667   1.346805  ...       NaN       NaN       NaN   
3     0.905745   8.049327  10.674124  ...       NaN       NaN       NaN   
4          NaN        NaN        NaN  ...       NaN       NaN       NaN   
..         ...        ...        ...  ...       ...       ...       ...   
376   2.312878   0.758202   1.648879  ...       NaN       NaN       NaN   
377        NaN        NaN        NaN  ...       NaN       NaN       NaN   
378   4.691976   1.170187   2.120301  ...       NaN       NaN       NaN   
379        NaN        NaN        NaN  ...       NaN       NaN       NaN   
380   0.880535   1.597975   1.538004  ...       NaN       NaN       NaN   

     pos183:S  pos184:C  pos185:K  pos186:C  pos187:V  pos188:L  pos189:S  
0         NaN       NaN       NaN       NaN       NaN       NaN       NaN  
1         NaN       NaN       NaN       NaN       NaN       NaN       NaN  
2         NaN       NaN       NaN       NaN       NaN       NaN       NaN  
3         NaN       NaN       NaN       NaN       NaN       NaN       NaN  
4         NaN       NaN       NaN       NaN       NaN       NaN       NaN  
..        ...       ...       ...       ...       ...       ...       ...  
376       NaN       NaN       NaN       NaN       NaN       NaN       NaN  
377       NaN       NaN       NaN       NaN       NaN       NaN       NaN  
378       NaN       NaN       NaN       NaN       NaN       NaN       NaN  
379       NaN       NaN       NaN       NaN       NaN       NaN       NaN  
380       NaN       NaN       NaN       NaN       NaN       NaN       NaN  

[381 rows x 190 columns]>
In [4]:
print(df.head)

<bound method NDFrame.head of     Unnamed: 0    pos1:M    pos2:T    pos3:E     pos4:Y     pos5:K     pos6:L  \
0         1A2B  2.779494  1.346638  1.505610        NaN   4.792497   2.745932   
1         1AA9  0.682297  8.415422  5.525405  11.984290  13.249885  20.284461   
2         1AGP  6.256867  0.542625  1.078158   1.291350   0.461579   1.732784   
3         1AM4       NaN       NaN       NaN  12.195632        NaN        NaN   
4         1AN0  1.145109       NaN       NaN        NaN        NaN        NaN   
..         ...       ...       ...       ...        ...        ...        ...   
376       8DNJ  0.981081  0.633802  0.643428   1.328113   2.103719   1.133679   
377       8EBZ       NaN       NaN       NaN        NaN        NaN   0.031832   
378       8EZG  6.499718  0.314923  0.853837   3.933503   7.116811   6.087974   
379       8F0M       NaN       NaN  0.020218        NaN        NaN        NaN   
380       8IJ9  0.012935  1.246463  1.178675   4.646635   0.613046   3.000449   

        pos7:V     pos8:V     pos9:V  ...  pos180:G  pos181:C  pos182:M  \
0     1.606917   3.031467   1.573611  ...       NaN       NaN       NaN   
1    35.385963  25.245161   9.270932  ...       NaN       NaN       NaN   
2     2.535707   0.765667   1.346805  ...       NaN       NaN       NaN   
3     0.905745   8.049327  10.674124  ...       NaN       NaN       NaN   
4          NaN        NaN        NaN  ...       NaN       NaN       NaN   
..         ...        ...        ...  ...       ...       ...       ...   
376   2.312878   0.758202   1.648879  ...       NaN       NaN       NaN   
377        NaN        NaN        NaN  ...       NaN       NaN       NaN   
378   4.691976   1.170187   2.120301  ...       NaN       NaN       NaN   
379        NaN        NaN        NaN  ...       NaN       NaN       NaN   
380   0.880535   1.597975   1.538004  ...       NaN       NaN       NaN   

     pos183:S  pos184:C  pos185:K  pos186:C  pos187:V  pos188:L  pos189:S  
0         NaN       NaN       NaN       NaN       NaN       NaN       NaN  
1         NaN       NaN       NaN       NaN       NaN       NaN       NaN  
2         NaN       NaN       NaN       NaN       NaN       NaN       NaN  
3         NaN       NaN       NaN       NaN       NaN       NaN       NaN  
4         NaN       NaN       NaN       NaN       NaN       NaN       NaN  
..        ...       ...       ...       ...       ...       ...       ...  
376       NaN       NaN       NaN       NaN       NaN       NaN       NaN  
377       NaN       NaN       NaN       NaN       NaN       NaN       NaN  
378       NaN       NaN       NaN       NaN       NaN       NaN       NaN  
379       NaN       NaN       NaN       NaN       NaN       NaN       NaN  
380       NaN       NaN       NaN       NaN       NaN       NaN       NaN  

[381 rows x 190 columns]>
In [5]:
metadata_df = pd.read_csv('C:/Users/Lympha/Desktop/temp_dir/result_dataframes/metadata_dataframe.csv')


metadata_df.head()
Out[5]:
Unnamed: 0 Title Structure Details Source Organism Taxonomy ID Abstract Method Resolution Original Number of Models Original Number of Chains ... Number of ILE Number of GLN Number of ASN Number of HIS Number of PHE Number of ASP Number of PRO Number of ARG Number of CYS Number of TRP
0 1A2B HUMAN RHOA COMPLEXED WITH GTP ANALOGUE NaN Homo sapiens 9606 The 2.4-A resolution crystal structure of a do... x-ray diffraction 2.4 1 1 ... 10 5 6 2.0 7 15 11.0 10 5.0 2.0
1 1AA9 HUMAN C-HA-RAS(1-171)(DOT)GDP, NMR, MINIMIZED ... NaN Homo sapiens 9606 The backbone 1H, 13C, and 15N resonances of th... solution nmr NaN 1 1 ... 11 11 4 3.0 5 14 3.0 12 3.0 NaN
2 1AGP THREE-DIMENSIONAL STRUCTURES AND PROPERTIES OF... C-H-RAS P21 PROTEIN MUTANT WITH GLY 12 REPLACE... Homo sapiens 9606 The three-dimensional structures and biochemic... x-ray diffraction 2.3 1 1 ... 11 11 4 3.0 5 15 3.0 11 3.0 NaN
3 1AM4 COMPLEX BETWEEN CDC42HS.GMPPNP AND P50 RHOGAP ... NaN Homo sapiens 9606 Small G proteins transduce signals from plasma... x-ray diffraction 2.7 1 6 ... 8 6 5 2.0 8 11 12.0 5 5.0 1.0
4 1AN0 CDC42HS-GDP COMPLEX NaN Homo sapiens 9606 No DOI found x-ray diffraction 2.8 1 2 ... 8 6 5 2.0 8 11 15.0 6 6.0 1.0

5 rows × 42 columns

In [6]:
plt.figure(figsize=(100,70))
sns.heatmap(df.drop(columns=['Unnamed: 0']), cmap='viridis')
plt.title('Repulsive Van der Waals on HRAS Experimental Structures')
plt.xlabel('Metric')
plt.ylabel('Structures')
plt.show
Out[6]:
<function matplotlib.pyplot.show(close=None, block=None)>
In [7]:
plt.figure(figsize=(15,10))
sns.histplot(df.drop(columns=['Unnamed: 0']).values.flatten(), bins=50, kde=True)
plt.xlabel('Value')
plt.ylabel('Density')
plt.title('Distribution of Values in Repulsive Van der Waals dataframe')
plt.show()
In [8]:
nan_percentage = df.isnull().mean() * 100


plt.figure(figsize=(100,70))
sns.barplot(x=nan_percentage.index, y=nan_percentage.values)
plt.xticks(rotation=90)
plt.ylabel('Percentage of NaN values')
plt.title('Percentage of NaN values in each column of Repulsive Van der Waals dataframe')
plt.show()
In [9]:
merged_nonnorm_df = pd.merge(df, metadata_df[['Unnamed: 0', 'Read Activity Status']], on='Unnamed: 0')

melted_nonnorm = pd.melt(merged_nonnorm_df, id_vars=['Unnamed: 0', 'Read Activity Status'], var_name='Amino Acid Position', value_name='Value')


plt.figure(figsize=(50,40))


sns.violinplot(x="Amino Acid Position", y="Value", hue="Read Activity Status", data=melted_nonnorm, split=True, inner="quart", palette={"active": "red", "inactive": "blue"})


plt.xticks(rotation=90)


plt.title('Violin Plot for All Amino Acid Positions')
plt.xlabel('Amino Acid Position')
plt.ylabel('Value')
plt.legend(title='Read Activity Status')


plt.tight_layout()


plt.show()
In [10]:
protein_codes = df['Unnamed: 0']


feature_df_numeric = df.drop(columns=['Unnamed: 0'])


scaler = StandardScaler()
feature_normalized = scaler.fit_transform(feature_df_numeric)


feature_normalized_df = pd.DataFrame(feature_normalized, columns=feature_df_numeric.columns)


feature_normalized_df.fillna(0, inplace=True)

C:\Users\Lympha\anaconda3\lib\site-packages\sklearn\utils\extmath.py:985: RuntimeWarning: invalid value encountered in true_divide
  updated_mean = (last_sum + new_sum) / updated_sample_count
C:\Users\Lympha\anaconda3\lib\site-packages\sklearn\utils\extmath.py:990: RuntimeWarning: invalid value encountered in true_divide
  T = new_sum / new_sample_count
C:\Users\Lympha\anaconda3\lib\site-packages\sklearn\utils\extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide
  new_unnormalized_variance -= correction ** 2 / new_sample_count
In [11]:
plt.figure(figsize=(100,70))
sns.heatmap(feature_normalized_df, cmap="YlGnBu", cbar_kws={'label': 'Z-score'})
plt.title('Heatmap of Normalized Repulsive Van der Waals dataframe')
plt.show()
In [12]:
plt.figure(figsize=(15,10))
sns.histplot(feature_normalized_df.values.flatten(), bins=50, kde=True)
plt.xlabel('Value')
plt.ylabel('Density')
plt.title('Distribution of Values in Repulsive Van der Waals dataframe')
plt.show()
In [13]:
merged_df = pd.merge(feature_normalized_df, metadata_df, left_on=protein_codes, right_on="Unnamed: 0")


X = feature_normalized_df
y = metadata_df["Read Activity Status"]
y_factorized = pd.factorize(y)[0]


merged_df.head()
Out[13]:
pos1:M pos2:T pos3:E pos4:Y pos5:K pos6:L pos7:V pos8:V pos9:V pos10:G ... Number of ILE Number of GLN Number of ASN Number of HIS Number of PHE Number of ASP Number of PRO Number of ARG Number of CYS Number of TRP
0 -0.045224 0.001432 -0.125505 0.000000 0.654193 0.017174 -0.413842 0.304740 -0.253153 -0.208667 ... 10 5 6 2.0 7 15 11.0 10 5.0 2.0
1 -0.103392 2.696359 0.402303 1.814929 3.010873 5.567569 10.062617 8.175365 3.152923 6.623976 ... 11 11 4 3.0 5 14 3.0 12 3.0 NaN
2 0.051225 -0.305093 -0.181631 -0.304797 -0.552631 -0.303456 -0.125781 -0.498065 -0.353515 0.004324 ... 11 11 4 3.0 5 15 3.0 11 3.0 NaN
3 0.000000 0.000000 0.000000 1.856825 0.000000 0.000000 -0.631308 2.082639 3.773837 0.000000 ... 8 6 5 2.0 8 11 12.0 5 5.0 1.0
4 -0.090555 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.448655 ... 8 6 5 2.0 8 11 15.0 6 6.0 1.0

5 rows × 231 columns

In [14]:
melted_data = pd.melt(X.iloc[:, :len(feature_normalized_df.columns)], value_vars=X.iloc[:, :len(feature_normalized_df.columns)].columns)
melted_data['Activity Status'] = np.tile(y, len(X.columns[:len(feature_normalized_df.columns)]))


plt.figure(figsize=(50,40))
sns.violinplot(x="variable", y="value", hue="Activity Status", data=melted_data, split=True, inner="quart", palette={"active": "red", "inactive": "blue"})
plt.xticks(rotation=90)
plt.title('Violin Plot for All Amino Acid Positions')
plt.xlabel('Amino Acid Position')
plt.ylabel('Value')
plt.legend(title='Activity Status')
plt.tight_layout()
plt.show()
In [15]:
correlation_matrix = X.iloc[:, :len(feature_normalized_df.columns)].corr()


correlation_with_target = X.iloc[:, :len(feature_normalized_df.columns)].apply(lambda x: x.corr(pd.Series(y_factorized)))


plt.figure(figsize=(100, 70))
sns.heatmap(correlation_matrix, cmap="coolwarm", vmin=-1, vmax=1, cbar_kws={'label': 'Correlation'})
plt.title('Correlation Matrix of Amino Acid Positions')
plt.show()


correlation_with_target_abs = correlation_with_target.abs().sort_values(ascending=False)
correlation_with_target_sorted = correlation_with_target[correlation_with_target_abs.index]
correlation_with_target_sorted.head(10)
Out[15]:
pos64:Y     0.206256
pos9:V      0.172304
pos33:D    -0.151059
pos67:M     0.138754
pos17:S    -0.137948
pos118:C    0.123786
pos58:T    -0.123456
pos74:T    -0.120982
pos120:L   -0.117875
pos72:M     0.114080
dtype: float64
In [16]:
linked = linkage(feature_normalized, method='ward')


color_map = {
    "active": "red",
    "inactive": "blue"
}


labels = df["Unnamed: 0"].values


plt.figure(figsize=(20,15))
dendro_data = dendrogram(linked, orientation='top', distance_sort='descending', show_leaf_counts=True, labels=labels)


ax = plt.gca()
xlbls = ax.get_xmajorticklabels()
for lbl in xlbls:
    structure_id = lbl.get_text()
    color = color_map[merged_df[merged_df["Unnamed: 0"] == structure_id]["Read Activity Status"].values[0]]
    lbl.set_color(color)

plt.title('Full Hierarchical Clustering Dendrogram for Repulsive Van der Waals dataframe (Colored by Activation Status)')
plt.xlabel('Protein Structure Names')
plt.ylabel('Distance (Ward)')
plt.xticks(rotation=90)  # Rotate x-axis labels for better readability
plt.show()
In [17]:
merged_df = pd.merge(feature_normalized_df, metadata_df, left_on=protein_codes, right_on="Unnamed: 0")


X = feature_normalized_df
y = merged_df["Read Activity Status"]


merged_df.head()
Out[17]:
pos1:M pos2:T pos3:E pos4:Y pos5:K pos6:L pos7:V pos8:V pos9:V pos10:G ... Number of ILE Number of GLN Number of ASN Number of HIS Number of PHE Number of ASP Number of PRO Number of ARG Number of CYS Number of TRP
0 -0.045224 0.001432 -0.125505 0.000000 0.654193 0.017174 -0.413842 0.304740 -0.253153 -0.208667 ... 10 5 6 2.0 7 15 11.0 10 5.0 2.0
1 -0.103392 2.696359 0.402303 1.814929 3.010873 5.567569 10.062617 8.175365 3.152923 6.623976 ... 11 11 4 3.0 5 14 3.0 12 3.0 NaN
2 0.051225 -0.305093 -0.181631 -0.304797 -0.552631 -0.303456 -0.125781 -0.498065 -0.353515 0.004324 ... 11 11 4 3.0 5 15 3.0 11 3.0 NaN
3 0.000000 0.000000 0.000000 1.856825 0.000000 0.000000 -0.631308 2.082639 3.773837 0.000000 ... 8 6 5 2.0 8 11 12.0 5 5.0 1.0
4 -0.090555 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.448655 ... 8 6 5 2.0 8 11 15.0 6 6.0 1.0

5 rows × 231 columns

In [18]:
pca = PCA(n_components=2)
principal_components = pca.fit_transform(X.iloc[:, :len(feature_normalized_df.columns)])


pca_df = pd.DataFrame(data=principal_components, columns=['Principal Component 1', 'Principal Component 2'])
pca_df['Activity Status'] = y


explained_variance = pca.explained_variance_ratio_


plt.figure(figsize=(10, 7))
sns.scatterplot(x='Principal Component 1', y='Principal Component 2', hue='Activity Status', data=pca_df, palette={"active": "red", "inactive": "blue"})


plt.xlabel(f'Principal Component 1 ({explained_variance[0]*100:.2f}%)')
plt.ylabel(f'Principal Component 2 ({explained_variance[1]*100:.2f}%)')

plt.title('2D PCA of Amino Acid Positions')
plt.show()
In [19]:
pca_3d = PCA(n_components=3)
principal_components_3d = pca_3d.fit_transform(X)


pca_df_3d = pd.DataFrame(data=principal_components_3d, columns=['Principal Component 1', 'Principal Component 2', 'Principal Component 3'])
pca_df_3d['Activity Status'] = y


colors = {'inactive': 'blue', 'active': 'red'}


explained_variance_3d = pca_3d.explained_variance_ratio_


fig = plt.figure(figsize=(15,10))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(pca_df_3d['Principal Component 1'], pca_df_3d['Principal Component 2'], pca_df_3d['Principal Component 3'], c=pca_df_3d["Activity Status"].map(colors), s=50, label=pca_df_3d["Activity Status"].unique())
ax.set_xlabel(f'Principal Component 1 ({explained_variance_3d[0]*100:.2f}%)')
ax.set_ylabel(f'Principal Component 2 ({explained_variance_3d[1]*100:.2f}%)')
ax.set_zlabel(f'Principal Component 3 ({explained_variance_3d[2]*100:.2f}%)')
ax.set_title('3D PCA of Amino Acid Positions')
legend_handles = [plt.Line2D([0], [0], marker='o', color='w', label=status, markersize=10, markerfacecolor=colors[status]) for status in colors]
ax.legend(handles=legend_handles, title='Activity Status')

plt.show()
In [20]:
tsne = TSNE(n_components=2, random_state=1)
tsne_2d = tsne.fit_transform(X)


tsne_df = pd.DataFrame(data=tsne_2d, columns=['t-SNE 1', 't-SNE 2'])
tsne_df['Activity Status'] = y


plt.figure(figsize=(10, 7))
sns.scatterplot(x='t-SNE 1', y='t-SNE 2', hue='Activity Status', data=tsne_df, palette={"active": "red", "inactive": "blue"})
plt.title('t-SNE Projection of Repulsive Van der Waals  Data')
plt.show()

C:\Users\Lympha\anaconda3\lib\site-packages\sklearn\manifold\_t_sne.py:780: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.
  warnings.warn(
C:\Users\Lympha\anaconda3\lib\site-packages\sklearn\manifold\_t_sne.py:790: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.
  warnings.warn(
In [21]:
label_encoder = LabelEncoder()
y_factorized = label_encoder.fit_transform(y)


rf_clf = RandomForestClassifier(n_estimators=100, random_state=1)
rf_clf.fit(X.iloc[:, :len(feature_normalized_df.columns)], y_factorized)


feature_importances = rf_clf.feature_importances_


importance_df = pd.DataFrame({
    'Amino Acid Position': X.columns[:len(feature_normalized_df.columns)],
    'Importance': feature_importances
})


sorted_importance_df = importance_df.sort_values(by='Importance', ascending=False)


top_n = 15
selected_aminoacids = sorted_importance_df['Amino Acid Position'][:top_n]
sorted_importance_df
Out[21]:
Amino Acid Position Importance
32 pos33:D 0.027339
63 pos64:Y 0.020264
38 pos39:S 0.019666
16 pos17:S 0.018863
11 pos12:G 0.016386
... ... ...
174 pos175:D 0.000000
173 pos174:P 0.000000
172 pos173:P 0.000000
170 pos171:L 0.000000
188 pos189:S 0.000000

189 rows × 2 columns

In [22]:
top_features = sorted_importance_df.head(top_n)


colors = cm.Set2(np.linspace(0, 1, top_n))


plt.figure(figsize=(10, 8))
bars = plt.barh(top_features['Amino Acid Position'], top_features['Importance'], color=colors)
plt.gca().invert_yaxis()  # to have the most important feature at the top
plt.title('Top {} Amino Acid Positions by Importance in Van der Waals Repulsive Profile'.format(top_n))
plt.xlabel('Importance')
plt.ylabel('Amino Acid Position')
plt.tight_layout()
plt.show()
In [23]:
colors = cm.Set2(np.linspace(0, 1, len(selected_aminoacids)))


hex_colors = [to_hex(color) for color in colors]


color_dict = dict(zip(selected_aminoacids, hex_colors))


plt.figure(figsize=(15, 10))
for position in selected_aminoacids:
    sns.distplot(X[position], label=position, hist=False, color=color_dict[position])

plt.title('Distribution of Selected Amino Acid Positions')
plt.xlabel('Value')
plt.ylabel('Density')
plt.legend()
plt.show()

C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
In [24]:
selected_correlation_matrix = correlation_matrix.loc[selected_aminoacids, selected_aminoacids]


plt.figure(figsize=(10, 8))
sns.heatmap(selected_correlation_matrix, cmap="coolwarm", annot=True, vmin=-1, vmax=1, cbar_kws={'label': 'Correlation'})
plt.title('Correlation Matrix of Important Amino Acid Positions')
plt.show()
In [25]:
plt.figure(figsize=(10, 6))
for idx, position in enumerate(selected_aminoacids):
    plt.subplot(3, 5, idx+1)
    sns.boxplot(x=y_factorized, y=X[position])
    plt.title(f'{position}:{round(sorted_importance_df.iloc[idx, sorted_importance_df.columns.get_loc("Importance")], 4)}')
    plt.xlabel('Activity Status')
    plt.ylabel('Value')

plt.tight_layout()
plt.show()
In [26]:
melted_data_selected = pd.melt(X[selected_aminoacids], value_vars=selected_aminoacids)
melted_data_selected['Activity Status'] = np.tile(y, len(selected_aminoacids))


plt.figure(figsize=(10, 6))
sns.violinplot(x="variable", y="value", hue="Activity Status", data=melted_data_selected, split=True, inner="quart", palette={"active": "red", "inactive": "blue"})
plt.title('Violin Plot for Selected Amino Acid Positions')
plt.xlabel('Amino Acid Position')
plt.ylabel('Value')
plt.legend(title='Activity Status')
plt.tight_layout()
plt.show()
In [27]:
active_data = X[y == "active"][selected_aminoacids]
inactive_data = X[y == "inactive"][selected_aminoacids]


t_stats = []
p_values = []

for position in selected_aminoacids:
    t_stat, p_value = ttest_ind(active_data[position], inactive_data[position])
    t_stats.append(t_stat)
    p_values.append(p_value)


t_test_results = pd.DataFrame({
    'Amino Acid Position': selected_aminoacids,
    'T-Statistic': t_stats,
    'P-Value': p_values
})

t_test_results
Out[27]:
Amino Acid Position T-Statistic P-Value
32 pos33:D -2.974937 0.003118
63 pos64:Y 4.103617 0.000050
38 pos39:S -1.607361 0.108808
16 pos17:S -2.711491 0.007003
11 pos12:G 0.157134 0.875223
26 pos27:H -1.650463 0.099677
80 pos81:V -1.135893 0.256719
8 pos9:V 3.405341 0.000732
20 pos21:I -1.302373 0.193580
59 pos60:G -1.309933 0.191012
106 pos107:D -1.911909 0.056642
34 pos35:T 2.005316 0.045640
119 pos120:L -2.310901 0.021374
71 pos72:M 2.235492 0.025967
70 pos71:Y 1.928639 0.054522
In [32]:
colors = cm.Set2(np.linspace(0, 1, top_n))


fig, ax1 = plt.subplots(figsize=(12, 8))


bars = ax1.bar(t_test_results['Amino Acid Position'], t_test_results['T-Statistic'], color=colors, label='T-Statistic')


ax2 = ax1.twinx()
ax2.scatter(t_test_results['Amino Acid Position'], t_test_results['P-Value'], color='red', marker='o', label='P-Value')
ax2.axhline(y=0.05, color='black', linestyle='--')  # significance threshold


ax2.set_ylim(0, 1)


ax1.set_ylabel('T-Statistic')
ax2.set_ylabel('P-Value', color='red')
ax1.set_xlabel('Amino Acid Position')
ax1.set_title(f'T-Test Results for Top {top_n} Amino Acid Positions')
ax1.legend(loc='upper left')
ax2.legend(loc='upper right')

plt.tight_layout()
plt.show()
In [29]:
bonferroni_corrected_pvalues = multipletests(t_test_results['P-Value'], method='bonferroni')[1]


fdr_corrected_pvalues = multipletests(t_test_results['P-Value'], method='fdr_bh')[1]


t_test_results['Bonferroni Corrected P-Value'] = bonferroni_corrected_pvalues
t_test_results['FDR Corrected P-Value'] = fdr_corrected_pvalues

t_test_results
Out[29]:
Amino Acid Position T-Statistic P-Value Bonferroni Corrected P-Value FDR Corrected P-Value
32 pos33:D -2.974937 0.003118 0.046773 0.015591
63 pos64:Y 4.103617 0.000050 0.000747 0.000747
38 pos39:S -1.607361 0.108808 1.000000 0.148374
16 pos17:S -2.711491 0.007003 0.105045 0.026261
11 pos12:G 0.157134 0.875223 1.000000 0.875223
26 pos27:H -1.650463 0.099677 1.000000 0.148374
80 pos81:V -1.135893 0.256719 1.000000 0.275056
8 pos9:V 3.405341 0.000732 0.010973 0.005486
20 pos21:I -1.302373 0.193580 1.000000 0.223361
59 pos60:G -1.309933 0.191012 1.000000 0.223361
106 pos107:D -1.911909 0.056642 0.849624 0.094403
34 pos35:T 2.005316 0.045640 0.684597 0.094403
119 pos120:L -2.310901 0.021374 0.320615 0.064123
71 pos72:M 2.235492 0.025967 0.389502 0.064917
70 pos71:Y 1.928639 0.054522 0.817833 0.094403
In [30]:
t_test_results.to_clipboard()
In [31]:
colors = cm.Set2(np.linspace(0, 1, top_n))


fig, ax1 = plt.subplots(figsize=(12,8))


bars = ax1.bar(t_test_results['Amino Acid Position'], t_test_results['T-Statistic'], color=colors, label='T-Statistic')


ax2 = ax1.twinx()
ax2.scatter(t_test_results['Amino Acid Position'], t_test_results['FDR Corrected P-Value'], color='red', marker='o', label='FDR Corrected P-Value')
ax2.axhline(y=0.05, color='black', linestyle='--')  # significance threshold


ax2.set_ylim(0, 1)


ax1.set_ylabel('T-Statistic')
ax2.set_ylabel('P-Value', color='red')
ax1.set_xlabel('Amino Acid Position')
ax1.set_title(f'T-Test Results for Top {top_n} Amino Acid Positions in Van der Waals Repulsive Profile')
ax1.legend(loc='upper left')
ax2.legend(loc='upper right')

plt.tight_layout()
plt.show()